| Challenge: | Detecting contradictions in text is difficult since it requires clinical expertise. |
| Approach: | They propose to use a medical ontology to build a seed of potential medical contradictions in medical abstracts by distant supervision. |
| Outcome: | The proposed method weakly supervises state-of-the-art deep learning models and shows significant improvements across multiple medical contradiction datasets. |
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| Challenge: | During times of pandemic, treatment options are limited, and developing new drug treatments is infeasible in the short-term. |
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| Challenge: | Large Language Models (LLMs) are obstructed by their opaque and often unreliable reasoning. |
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A Large Collection of Model-generated Contradictory Responses for Consistency-aware Dialogue Systems (2024.findings-acl)
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| Challenge: | Recent large-scale neural response generation models (RGMs) have made significant progress but still struggle to generate semantically appropriate responses. |
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Red Teaming Language Models for Processing Contradictory Dialogues (2024.emnlp-main)
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Assessing and Mitigating Medical Knowledge Drift and Conflicts in Large Language Models (2025.findings-emnlp)
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| Challenge: | Rapid medical concept drift can lead LLMs to provide incorrect or outdated advice. |
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Med-CoDE: Medical Critique based Disagreement Evaluation Framework (2025.naacl-srw)
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| Challenge: | Existing evaluation methods for large language models lack robustness and accuracy in medical contexts. |
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Lessons from Natural Language Inference in the Clinical Domain (D18-1)
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A Distant Supervision Corpus for Extracting Biomedical Relationships Between Chemicals, Diseases and Genes (2022.lrec-1)
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| Challenge: | Biomedical researchers have used manual curation to extract biomedical interactions from research texts to improve coverage. |
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